As the percentage of the U.S. population aged 65 and older grows from the 12.9 percent reported in 2009 to the estimated 19 percent projected in 20301, it is becoming increasingly important that the many factors affecting the health and well being of this portion of the population are understood and addressed. Methods of surveying this population and gathering longitudinal data regarding aspects of their lives, such as activity level sleep patterns, physiological data, and behavior patterns are needed and have been highlighted as an area of interest by the National Institute of Aging. Such data are not only useful to academic researchers, but to insurers, health care providers, health and health policy analysts and, on a more intimate level, by caregivers. However, cost and adherence play key roles in the ability to collect such data, so an easy-to-use, low-cost automatic data collection system is required; one which can and will be utilized by the elderly on a regular basis and which is capable of capturing a wide range of key health indicators. This Phase II application aims to demonstrate the effectiveness of a portable and real-time survey system capable of collecting activity level, location, and sleep patterns on a continual basis from a wrist-based device in a watch form factor, as well as physiological data such as blood pressure and weight from easy-to-use wireless devices, and self-reported data from easy, touch-screen interfaces regarding a number of varying topics including pain level, diet and nutrition, or stress. The application aims to prove the following hypothesis: The proposed monitoring system provides an effective means of collecting real-time survey data (including physiological, activity, sleep, and self- report dat) from elderly individuals for an extended period of time and is capable of recognizing deviations from individualized baseline norms that could be indicative of illness or need for intervention. During the study, 30 reasonably healthy elderly individuals and 60 elderly individuals with congestive heart failure (CHF) will be monitored over six months, during which a number of episodes of acute CHF exacerbation are expected to occur; the system will learn to recognize and alert upon such episodes. CHF was chosen so that the effectiveness of the system in flagging deviations from an individual's system-learned baseline could be better demonstrated. All study participants will be monitored by a visiting nurse/physician once each week. Moreover, 30 of the CHF participants and the 30 other participants will also be monitored using the proposed system configured to provide real-time data to the nurse in charge of each participant. The specific aims of the project will be to establish that: 1) the platform can and will be used by an elderly population for an extended (six-month) period of time, 2) the watch device is effective for collecting longitudinal sleep and activity data, and 3) the system is effective in providing useful survey data to monitoring nurses while reducing the burden of care.